Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.

$\begingroup$In addition to learning Machine Learning by Andrew Ng you can try with some courses in data science signature track in kaggle.Also a quick way to learn practical machine learning is to take part in following machine learning competition at kaggle,as that has nice guide material on how to do feature selection,data munging and building final model in R and in Python.kaggle.com/c/titanic/details/getting-started-with-python$\endgroup$
– 0xFJul 17 '15 at 0:49

$\begingroup$+1 For Andrew Ng's course. It is very well done.$\endgroup$
– TylerAndFriendsJun 10 '14 at 15:12

1

$\begingroup$John Hopkins also has a data science certificate track (9 classes) that started last week at Coursera. coursera.org/specialization/jhudatascience/… - it's not all machine learning, but worth sharing. Coursera is full of awesomeness (and Andrew Ng is a great lecturer).$\endgroup$
– Steve KallestadJun 11 '14 at 20:49

To be honest, I think that doing some projects will teach you much more than doing a full course. One reason is that doing a project is more motivating and open-ended than doing assignments.

A course, if you have the time AND motivation (real motivation), is better than doing a project. The other commentators have made good platform recommendations on tech.

I think, from a fun project standpoint, you should ask a question and get a computer to learn to answer it.

Some good classic questions that have good examples are:

Neural Networks for recognizing hand written digits

Spam email classification using logistic regression

Classification of objects using Gaussian Mixture models

Some use of linear regression, perhaps forecasting of grocery prices given neighborhoods

These projects have the math done, code done, and can be found with Google readily.

Other cool subjects can be done by you!

Lastly, I research robotics, so for me the most FUN applications are behavioral.
Examples can include (if you can play with an arduino)

Create a application, that uses logistic regression perhaps, that learns when to turn the fan off and on given the inner temperature, and the status of the light in the room.

Create an application that teaches a robot to move an actuator, perhaps a wheel, based on sensor input (perhaps a button press), using Gaussian Mixture Models (learning from demonstration).

Anyway, those are pretty advanced. The point I'm making is that if you pick a project that you (really really) like, and spend a few week on it, you will learn a massive amount, and understand so much more than you will get doing a few assignments.

Assuming you're familiar with programming I would recommend looking at scikit-learn. It has especially nice help pages that can serve as mini-tutorials/a quick tour through machine learning. Pick an area you find interesting and work through the examples.

If you can reproduce the 6x3 grid of graphs from the banner of the http://scikit-learn.org/ page then you will have learnt some ML and some Python. You didn't mention a language. Python is easy enough to learn very quickly, and scikit-learn has a wide range of algorithms implemented.

In addition to the courses and tutorials posted, I would suggest something a bit more 'hands on': Kaggle has some introductory competitions that might pique your interest (most people start with the Titanic competition). And there's a large variety of subjects to explore and compete in when you want to get more experience.

As mentioned in above answers grasp the basics of ML by following MOOCs by Prof.Andrew Ng and 'Learning From Data' by Prof. Yaser Abu-Mostafa.

R is the clear winner as the most used tool in Kaggle competitions. (Don't forget to check the resources on Kaggle wiki and forums)

Learn basic R and Python. Coursera 'Data Science' track has an introductory R course. Almost all the algorithms can be found in Python and R libraries. Feel free to use the algorithms you learned in few kaggle competitions. As a starting point compare the performance of several algorithms on Titanic dataset and Digit recognizer dataset on kaggle.